Overview

Dataset statistics

Number of variables14
Number of observations2919
Missing cells1644
Missing cells (%)4.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory319.4 KiB
Average record size in memory112.0 B

Variable types

Numeric13
Categorical1

Warnings

GarageYrBlt has 159 (5.4%) missing values Missing
SalePrice has 1459 (50.0%) missing values Missing
df_index is uniformly distributed Uniform
GarageCars has 157 (5.4%) zeros Zeros
GarageArea has 157 (5.4%) zeros Zeros
TotalBsmtSF has 78 (2.7%) zeros Zeros
MasVnrArea has 1738 (59.5%) zeros Zeros

Reproduction

Analysis started2021-05-26 10:11:08.391017
Analysis finished2021-05-26 10:11:30.098016
Duration21.71 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIFORM

Distinct1460
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean729.2500856
Minimum0
Maximum1459
Zeros2
Zeros (%)0.1%
Memory size22.9 KiB
2021-05-26T12:11:30.169013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile72.9
Q1364.5
median729
Q31094
95-th percentile1386
Maximum1459
Range1459
Interquartile range (IQR)729.5

Descriptive statistics

Standard deviation421.3935957
Coefficient of variation (CV)0.5778451097
Kurtosis-1.199999154
Mean729.2500856
Median Absolute Deviation (MAD)365
Skewness1.220300236 × 106
Sum2128681
Variance177572.5625
MonotocityNot monotonic
2021-05-26T12:11:30.308024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14582
 
0.1%
9562
 
0.1%
9602
 
0.1%
9622
 
0.1%
9642
 
0.1%
9662
 
0.1%
9682
 
0.1%
9702
 
0.1%
9722
 
0.1%
9742
 
0.1%
Other values (1450)2899
99.3%
ValueCountFrequency (%)
02
0.1%
12
0.1%
22
0.1%
32
0.1%
42
0.1%
52
0.1%
62
0.1%
72
0.1%
82
0.1%
92
0.1%
ValueCountFrequency (%)
14591
< 0.1%
14582
0.1%
14572
0.1%
14562
0.1%
14552
0.1%
14542
0.1%
14532
0.1%
14522
0.1%
14512
0.1%
14502
0.1%

OverallQual
Real number (ℝ≥0)

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0890716
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Memory size22.9 KiB
2021-05-26T12:11:30.435015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.409947207
Coefficient of variation (CV)0.2315537243
Kurtosis0.06721935991
Mean6.0890716
Median Absolute Deviation (MAD)1
Skewness0.1972118053
Sum17774
Variance1.987951125
MonotocityNot monotonic
2021-05-26T12:11:30.532191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5825
28.3%
6731
25.0%
7600
20.6%
8342
11.7%
4226
 
7.7%
9107
 
3.7%
340
 
1.4%
1031
 
1.1%
213
 
0.4%
14
 
0.1%
ValueCountFrequency (%)
14
 
0.1%
213
 
0.4%
340
 
1.4%
4226
 
7.7%
5825
28.3%
6731
25.0%
7600
20.6%
8342
11.7%
9107
 
3.7%
1031
 
1.1%
ValueCountFrequency (%)
1031
 
1.1%
9107
 
3.7%
8342
11.7%
7600
20.6%
6731
25.0%
5825
28.3%
4226
 
7.7%
340
 
1.4%
213
 
0.4%
14
 
0.1%

GrLivArea
Real number (ℝ≥0)

Distinct1292
Distinct (%)44.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1500.759849
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Memory size22.9 KiB
2021-05-26T12:11:30.673178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile861
Q11126
median1444
Q31743.5
95-th percentile2464.2
Maximum5642
Range5308
Interquartile range (IQR)617.5

Descriptive statistics

Standard deviation506.0510451
Coefficient of variation (CV)0.337196551
Kurtosis4.121603735
Mean1500.759849
Median Absolute Deviation (MAD)313
Skewness1.270010408
Sum4380718
Variance256087.6603
MonotocityNot monotonic
2021-05-26T12:11:30.818183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86441
 
1.4%
109226
 
0.9%
104025
 
0.9%
145620
 
0.7%
120018
 
0.6%
89415
 
0.5%
91214
 
0.5%
81614
 
0.5%
172813
 
0.4%
84813
 
0.4%
Other values (1282)2720
93.2%
ValueCountFrequency (%)
3341
< 0.1%
4071
< 0.1%
4381
< 0.1%
4801
< 0.1%
4921
< 0.1%
4981
< 0.1%
5201
< 0.1%
5401
< 0.1%
5721
< 0.1%
5991
< 0.1%
ValueCountFrequency (%)
56421
< 0.1%
50951
< 0.1%
46761
< 0.1%
44761
< 0.1%
43161
< 0.1%
38201
< 0.1%
36721
< 0.1%
36271
< 0.1%
36081
< 0.1%
35001
< 0.1%

GarageCars
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.766620973
Minimum0
Maximum5
Zeros157
Zeros (%)5.4%
Memory size22.9 KiB
2021-05-26T12:11:30.932169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7616243226
Coefficient of variation (CV)0.4311192577
Kurtosis0.2381978193
Mean1.766620973
Median Absolute Deviation (MAD)0
Skewness-0.2183727666
Sum5155
Variance0.5800716088
MonotocityNot monotonic
2021-05-26T12:11:31.039166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
21594
54.6%
1776
26.6%
3374
 
12.8%
0157
 
5.4%
416
 
0.5%
51
 
< 0.1%
(Missing)1
 
< 0.1%
ValueCountFrequency (%)
0157
 
5.4%
1776
26.6%
21594
54.6%
3374
 
12.8%
416
 
0.5%
51
 
< 0.1%
ValueCountFrequency (%)
51
 
< 0.1%
416
 
0.5%
3374
 
12.8%
21594
54.6%
1776
26.6%
0157
 
5.4%

GarageArea
Real number (ℝ≥0)

ZEROS

Distinct603
Distinct (%)20.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean472.8745716
Minimum0
Maximum1488
Zeros157
Zeros (%)5.4%
Memory size22.9 KiB
2021-05-26T12:11:31.187164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1320
median480
Q3576
95-th percentile856.15
Maximum1488
Range1488
Interquartile range (IQR)256

Descriptive statistics

Standard deviation215.394815
Coefficient of variation (CV)0.4555009466
Kurtosis0.9397829054
Mean472.8745716
Median Absolute Deviation (MAD)124
Skewness0.2413005173
Sum1379848
Variance46394.92633
MonotocityNot monotonic
2021-05-26T12:11:31.341172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0157
 
5.4%
57697
 
3.3%
44096
 
3.3%
24069
 
2.4%
48468
 
2.3%
52865
 
2.2%
40058
 
2.0%
48054
 
1.8%
26451
 
1.7%
28850
 
1.7%
Other values (593)2153
73.8%
ValueCountFrequency (%)
0157
5.4%
1001
 
< 0.1%
1603
 
0.1%
1622
 
0.1%
1642
 
0.1%
18016
 
0.5%
1841
 
< 0.1%
1851
 
< 0.1%
1861
 
< 0.1%
1891
 
< 0.1%
ValueCountFrequency (%)
14881
< 0.1%
14181
< 0.1%
13901
< 0.1%
13561
< 0.1%
13481
< 0.1%
13141
< 0.1%
12481
< 0.1%
12311
< 0.1%
12201
< 0.1%
12001
< 0.1%

TotalBsmtSF
Real number (ℝ≥0)

ZEROS

Distinct1058
Distinct (%)36.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1051.777587
Minimum0
Maximum6110
Zeros78
Zeros (%)2.7%
Memory size22.9 KiB
2021-05-26T12:11:31.486163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile455.25
Q1793
median989.5
Q31302
95-th percentile1776.15
Maximum6110
Range6110
Interquartile range (IQR)509

Descriptive statistics

Standard deviation440.7662581
Coefficient of variation (CV)0.4190679317
Kurtosis9.151099191
Mean1051.777587
Median Absolute Deviation (MAD)236.5
Skewness1.162882475
Sum3069087
Variance194274.8943
MonotocityNot monotonic
2021-05-26T12:11:34.294436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
078
 
2.7%
86474
 
2.5%
67229
 
1.0%
91226
 
0.9%
104025
 
0.9%
76824
 
0.8%
81623
 
0.8%
72820
 
0.7%
38419
 
0.7%
100819
 
0.7%
Other values (1048)2581
88.4%
ValueCountFrequency (%)
078
2.7%
1051
 
< 0.1%
1601
 
< 0.1%
1731
 
< 0.1%
1901
 
< 0.1%
1921
 
< 0.1%
2162
 
0.1%
2401
 
< 0.1%
2451
 
< 0.1%
2644
 
0.1%
ValueCountFrequency (%)
61101
< 0.1%
50951
< 0.1%
32061
< 0.1%
32001
< 0.1%
31381
< 0.1%
30941
< 0.1%
28461
< 0.1%
26601
< 0.1%
26331
< 0.1%
26301
< 0.1%

1stFlrSF
Real number (ℝ≥0)

Distinct1083
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1159.581706
Minimum334
Maximum5095
Zeros0
Zeros (%)0.0%
Memory size22.9 KiB
2021-05-26T12:11:34.438430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile665.9
Q1876
median1082
Q31387.5
95-th percentile1830.1
Maximum5095
Range4761
Interquartile range (IQR)511.5

Descriptive statistics

Standard deviation392.3620787
Coefficient of variation (CV)0.3383651851
Kurtosis6.956479038
Mean1159.581706
Median Absolute Deviation (MAD)235
Skewness1.470360106
Sum3384819
Variance153948.0008
MonotocityNot monotonic
2021-05-26T12:11:34.573435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86446
 
1.6%
104028
 
1.0%
91219
 
0.7%
84818
 
0.6%
96018
 
0.6%
81618
 
0.6%
89417
 
0.6%
93617
 
0.6%
67217
 
0.6%
54615
 
0.5%
Other values (1073)2706
92.7%
ValueCountFrequency (%)
3341
 
< 0.1%
3721
 
< 0.1%
4071
 
< 0.1%
4321
 
< 0.1%
4381
 
< 0.1%
4421
 
< 0.1%
4481
 
< 0.1%
4531
 
< 0.1%
4801
 
< 0.1%
48313
0.4%
ValueCountFrequency (%)
50951
< 0.1%
46921
< 0.1%
38201
< 0.1%
32281
< 0.1%
31381
< 0.1%
28981
< 0.1%
27261
< 0.1%
26961
< 0.1%
26741
< 0.1%
26331
< 0.1%

FullBath
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size165.5 KiB
2
1530 
1
1309 
3
 
64
0
 
12
4
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2919
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2
ValueCountFrequency (%)
21530
52.4%
11309
44.8%
364
 
2.2%
012
 
0.4%
44
 
0.1%
2021-05-26T12:11:34.837418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-26T12:11:34.912425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
21530
52.4%
11309
44.8%
364
 
2.2%
012
 
0.4%
44
 
0.1%

Most occurring characters

ValueCountFrequency (%)
21530
52.4%
11309
44.8%
364
 
2.2%
012
 
0.4%
44
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2919
100.0%

Most frequent character per category

ValueCountFrequency (%)
21530
52.4%
11309
44.8%
364
 
2.2%
012
 
0.4%
44
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common2919
100.0%

Most frequent character per script

ValueCountFrequency (%)
21530
52.4%
11309
44.8%
364
 
2.2%
012
 
0.4%
44
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2919
100.0%

Most frequent character per block

ValueCountFrequency (%)
21530
52.4%
11309
44.8%
364
 
2.2%
012
 
0.4%
44
 
0.1%

TotRmsAbvGrd
Real number (ℝ≥0)

Distinct14
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.451524495
Minimum2
Maximum15
Zeros0
Zeros (%)0.0%
Memory size22.9 KiB
2021-05-26T12:11:35.015419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q37
95-th percentile9
Maximum15
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.569379144
Coefficient of variation (CV)0.2432571007
Kurtosis1.169063585
Mean6.451524495
Median Absolute Deviation (MAD)1
Skewness0.7587568677
Sum18832
Variance2.462950897
MonotocityNot monotonic
2021-05-26T12:11:35.124024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
6844
28.9%
7649
22.2%
5583
20.0%
8347
11.9%
4196
 
6.7%
9143
 
4.9%
1080
 
2.7%
1132
 
1.1%
325
 
0.9%
1216
 
0.5%
Other values (4)4
 
0.1%
ValueCountFrequency (%)
21
 
< 0.1%
325
 
0.9%
4196
 
6.7%
5583
20.0%
6844
28.9%
7649
22.2%
8347
11.9%
9143
 
4.9%
1080
 
2.7%
1132
 
1.1%
ValueCountFrequency (%)
151
 
< 0.1%
141
 
< 0.1%
131
 
< 0.1%
1216
 
0.5%
1132
 
1.1%
1080
 
2.7%
9143
 
4.9%
8347
11.9%
7649
22.2%
6844
28.9%

YearBuilt
Real number (ℝ≥0)

Distinct118
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.312778
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Memory size22.9 KiB
2021-05-26T12:11:35.244404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1915
Q11953.5
median1973
Q32001
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)47.5

Descriptive statistics

Standard deviation30.29144153
Coefficient of variation (CV)0.01536612651
Kurtosis-0.5113172971
Mean1971.312778
Median Absolute Deviation (MAD)25
Skewness-0.6001139749
Sum5754262
Variance917.5714302
MonotocityNot monotonic
2021-05-26T12:11:35.390390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005142
 
4.9%
2006138
 
4.7%
2007109
 
3.7%
200499
 
3.4%
200388
 
3.0%
197757
 
2.0%
192057
 
2.0%
197654
 
1.8%
199952
 
1.8%
200849
 
1.7%
Other values (108)2074
71.1%
ValueCountFrequency (%)
18721
 
< 0.1%
18751
 
< 0.1%
18791
 
< 0.1%
18805
0.2%
18821
 
< 0.1%
18852
 
0.1%
18907
0.2%
18922
 
0.1%
18931
 
< 0.1%
18953
0.1%
ValueCountFrequency (%)
20103
 
0.1%
200925
 
0.9%
200849
 
1.7%
2007109
3.7%
2006138
4.7%
2005142
4.9%
200499
3.4%
200388
3.0%
200247
 
1.6%
200135
 
1.2%

YearRemodAdd
Real number (ℝ≥0)

Distinct61
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.264474
Minimum1950
Maximum2010
Zeros0
Zeros (%)0.0%
Memory size22.9 KiB
2021-05-26T12:11:35.532626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1950
Q11965
median1993
Q32004
95-th percentile2007
Maximum2010
Range60
Interquartile range (IQR)39

Descriptive statistics

Standard deviation20.89434423
Coefficient of variation (CV)0.01053001982
Kurtosis-1.346431392
Mean1984.264474
Median Absolute Deviation (MAD)14
Skewness-0.4512522973
Sum5792068
Variance436.573621
MonotocityNot monotonic
2021-05-26T12:11:35.663796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950361
 
12.4%
2006202
 
6.9%
2007164
 
5.6%
2005141
 
4.8%
2004111
 
3.8%
2000104
 
3.6%
200399
 
3.4%
200282
 
2.8%
200881
 
2.8%
199877
 
2.6%
Other values (51)1497
51.3%
ValueCountFrequency (%)
1950361
12.4%
195114
 
0.5%
195215
 
0.5%
195320
 
0.7%
195428
 
1.0%
195525
 
0.9%
195630
 
1.0%
195720
 
0.7%
195834
 
1.2%
195930
 
1.0%
ValueCountFrequency (%)
201013
 
0.4%
200934
 
1.2%
200881
2.8%
2007164
5.6%
2006202
6.9%
2005141
4.8%
2004111
3.8%
200399
3.4%
200282
2.8%
200149
 
1.7%

GarageYrBlt
Real number (ℝ≥0)

MISSING

Distinct103
Distinct (%)3.7%
Missing159
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean1978.113406
Minimum1895
Maximum2207
Zeros0
Zeros (%)0.0%
Memory size22.9 KiB
2021-05-26T12:11:35.827789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1895
5-th percentile1928
Q11960
median1979
Q32002
95-th percentile2007
Maximum2207
Range312
Interquartile range (IQR)42

Descriptive statistics

Standard deviation25.57428472
Coefficient of variation (CV)0.01292862414
Kurtosis1.809844718
Mean1978.113406
Median Absolute Deviation (MAD)21
Skewness-0.382150161
Sum5459593
Variance654.0440391
MonotocityNot monotonic
2021-05-26T12:11:35.978782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005142
 
4.9%
2006115
 
3.9%
2007115
 
3.9%
200499
 
3.4%
200392
 
3.2%
197766
 
2.3%
200861
 
2.1%
199858
 
2.0%
200055
 
1.9%
199954
 
1.8%
Other values (93)1903
65.2%
(Missing)159
 
5.4%
ValueCountFrequency (%)
18951
 
< 0.1%
18961
 
< 0.1%
19006
0.2%
19061
 
< 0.1%
19081
 
< 0.1%
191010
0.3%
19142
 
0.1%
19157
0.2%
19166
0.2%
19172
 
0.1%
ValueCountFrequency (%)
22071
 
< 0.1%
20105
 
0.2%
200929
 
1.0%
200861
2.1%
2007115
3.9%
2006115
3.9%
2005142
4.9%
200499
3.4%
200392
3.2%
200253
 
1.8%

MasVnrArea
Real number (ℝ≥0)

ZEROS

Distinct444
Distinct (%)15.3%
Missing23
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean102.2013122
Minimum0
Maximum1600
Zeros1738
Zeros (%)59.5%
Memory size22.9 KiB
2021-05-26T12:11:36.127767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3164
95-th percentile466.5
Maximum1600
Range1600
Interquartile range (IQR)164

Descriptive statistics

Standard deviation179.334253
Coefficient of variation (CV)1.754715759
Kurtosis9.254343333
Mean102.2013122
Median Absolute Deviation (MAD)0
Skewness2.602588512
Sum295975
Variance32160.77431
MonotocityNot monotonic
2021-05-26T12:11:36.264773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01738
59.5%
12015
 
0.5%
20013
 
0.4%
17613
 
0.4%
18012
 
0.4%
21612
 
0.4%
14411
 
0.4%
7211
 
0.4%
10811
 
0.4%
1611
 
0.4%
Other values (434)1049
35.9%
(Missing)23
 
0.8%
ValueCountFrequency (%)
01738
59.5%
13
 
0.1%
31
 
< 0.1%
111
 
< 0.1%
144
 
0.1%
1611
 
0.4%
183
 
0.1%
204
 
0.1%
222
 
0.1%
234
 
0.1%
ValueCountFrequency (%)
16001
< 0.1%
13781
< 0.1%
12901
< 0.1%
12242
0.1%
11701
< 0.1%
11591
< 0.1%
11291
< 0.1%
11151
< 0.1%
11101
< 0.1%
10951
< 0.1%

SalePrice
Real number (ℝ≥0)

MISSING

Distinct663
Distinct (%)45.4%
Missing1459
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean180921.1959
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Memory size22.9 KiB
2021-05-26T12:11:36.407769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129975
median163000
Q3214000
95-th percentile326100
Maximum755000
Range720100
Interquartile range (IQR)84025

Descriptive statistics

Standard deviation79442.50288
Coefficient of variation (CV)0.4391000319
Kurtosis6.53628186
Mean180921.1959
Median Absolute Deviation (MAD)38000
Skewness1.88287576
Sum264144946
Variance6311111264
MonotocityNot monotonic
2021-05-26T12:11:36.539764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14000020
 
0.7%
13500017
 
0.6%
14500014
 
0.5%
15500014
 
0.5%
19000013
 
0.4%
11000013
 
0.4%
16000012
 
0.4%
11500012
 
0.4%
13000011
 
0.4%
13900011
 
0.4%
Other values (653)1323
45.3%
(Missing)1459
50.0%
ValueCountFrequency (%)
349001
< 0.1%
353111
< 0.1%
379001
< 0.1%
393001
< 0.1%
400001
< 0.1%
520001
< 0.1%
525001
< 0.1%
550002
0.1%
559931
< 0.1%
585001
< 0.1%
ValueCountFrequency (%)
7550001
< 0.1%
7450001
< 0.1%
6250001
< 0.1%
6116571
< 0.1%
5829331
< 0.1%
5565811
< 0.1%
5550001
< 0.1%
5380001
< 0.1%
5018371
< 0.1%
4850001
< 0.1%

Interactions

2021-05-26T12:11:08.981999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:09.168993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:09.324002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:09.470997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:09.621979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:09.793828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:09.947825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:10.089821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:10.240829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:10.379939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:10.516950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:10.655056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:10.811125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:10.934123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:11.070266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:11.203266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:11.330251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:11.457255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:11.574256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:11.704252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:11.825233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:11.946444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:12.068455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:12.190567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:12.309564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:12.427628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:12.534902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:12.644212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:12.764933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:12.880203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:12.987583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:13.101304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:13.224479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:13.343472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:13.458620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:13.603620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:13.727612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:13.847599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:13.961597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:14.073601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:14.195598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:14.315593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:14.425591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:14.542587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:14.660804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:14.780103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:14.913512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:15.038830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:15.213327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:15.352733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:15.472157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:15.591554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:15.714871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:15.843487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:15.990418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:16.117793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:16.252031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:16.408854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:16.542187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:16.690416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:16.823405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:16.975547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:17.103553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:17.223907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:17.346596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:17.477027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:17.594231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:17.729342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:17.856650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:17.991810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:18.117077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:18.259083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:18.388194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:18.506700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:18.615096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:18.728433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:18.838421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:18.959431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:19.074423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:19.189412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:19.308416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:19.424412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:19.539409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:19.660396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:19.777402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:19.900398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:20.014396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:20.135393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:20.265387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:20.394384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:20.521372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:20.644375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:20.783584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:20.947693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:21.074695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:21.227685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:21.376122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:21.502989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:21.618227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:21.738610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:21.860925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:21.990276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:22.114600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:22.231904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:22.356283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:22.482789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:22.610415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:22.747425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:22.912419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:23.052410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:23.177409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:23.340394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:23.465529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:23.615294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:23.742723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:23.861096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:23.984555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:24.121811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:24.253259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:24.393255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:24.524451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:24.651174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:24.767326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:24.911326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:25.049322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:25.182312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:25.316299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:25.440305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:25.564301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:25.696297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:25.820762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:25.947910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:26.072362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:26.211963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:26.340269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:26.463989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:26.593212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:26.729919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:26.862621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:26.987010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:27.119343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:27.250821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:27.385140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:27.514454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:27.653039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:27.788195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:27.913196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:28.039188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:28.175184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:28.308180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:28.440177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:28.558172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:28.691705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:28.822637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:28.954466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-26T12:11:29.083818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-05-26T12:11:36.675858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-26T12:11:36.910846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-26T12:11:37.143269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-26T12:11:37.375043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-05-26T12:11:29.315746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-26T12:11:29.617046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-05-26T12:11:29.827039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-05-26T12:11:29.964030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexOverallQualGrLivAreaGarageCarsGarageAreaTotalBsmtSF1stFlrSFFullBathTotRmsAbvGrdYearBuiltYearRemodAddGarageYrBltMasVnrAreaSalePrice
00717102.0548.0856.085628200320032003.0196.0208500.0
11612622.0460.01262.0126226197619761976.00.0181500.0
22717862.0608.0920.092026200120022001.0162.0223500.0
33717173.0642.0756.096117191519701998.00.0140000.0
44821983.0836.01145.0114529200020002000.0350.0250000.0
55513622.0480.0796.079615199319951993.00.0143000.0
66816942.0636.01686.0169427200420052004.0186.0307000.0
77720902.0484.01107.0110727197319731973.0240.0200000.0
88717742.0468.0952.0102228193119501931.00.0129900.0
99510771.0205.0991.0107715193919501939.00.0118000.0

Last rows

df_indexOverallQualGrLivAreaGarageCarsGarageAreaTotalBsmtSF1stFlrSFFullBathTotRmsAbvGrdYearBuiltYearRemodAddGarageYrBltMasVnrAreaSalePrice
2909144946300.00.0630.06301319701970NaN0.0NaN
29101450410921.0253.0546.054615197219721972.00.0NaN
29111451513601.0336.01104.0136018196919791969.0194.0NaN
29121452410921.0286.0546.054615197019701970.00.0NaN
29131453410920.00.0546.05461519701970NaN0.0NaN
29141454410920.00.0546.05461519701970NaN0.0NaN
29151455410921.0286.0546.054616197019701970.00.0NaN
29161456512242.0576.01224.0122417196019961960.00.0NaN
2917145759700.00.0912.09701619921992NaN0.0NaN
29181458720003.0650.0996.099629199319941993.094.0NaN